Databases and SQL
- Explain how databases represent missing information.
- Explain the three-valued logic databases use when manipulating missing information.
- Write queries that handle missing information correctly.
Real-world data is never complete — there are always holes. Databases represent these holes using a special value called
null is not zero,
False, or the empty string; it is a one-of-a-kind value that means “nothing here”. Dealing with
null requires a few special tricks and some careful thinking.
To start, let’s have a look at the
Visited table. There are eight records, but #752 doesn’t have a date — or rather, its date is null:
SELECT * FROM Visited;
Null doesn’t behave like other values. If we select the records that come before 1930:
SELECT * FROM Visited WHERE dated<'1930-01-01';
we get two results, and if we select the ones that come during or after 1930:
SELECT * FROM Visited WHERE dated>='1930-01-01';
we get five, but record #752 isn’t in either set of results. The reason is that
null<'1930-01-01' is neither true nor false: null means, “We don’t know,” and if we don’t know the value on the left side of a comparison, we don’t know whether the comparison is true or false. Since databases represent “don’t know” as null, the value of
null<'1930-01-01' is actually
null>='1930-01-01' is also null because we can’t answer to that question either. And since the only records kept by a
WHERE are those for which the test is true, record #752 isn’t included in either set of results.
Comparisons aren’t the only operations that behave this way with nulls.
null, and so on. In particular, comparing things to null with = and != produces null:
SELECT * FROM Visited WHERE dated=NULL;
produces no output, and neither does:
SELECT * FROM Visited WHERE dated!=NULL;
To check whether a value is
null or not, we must use a special test
SELECT * FROM Visited WHERE dated IS NULL;
or its inverse
IS NOT NULL:
SELECT * FROM Visited WHERE dated IS NOT NULL;
Null values can cause headaches wherever they appear. For example, suppose we want to find all the salinity measurements that weren’t taken by Lake. It’s natural to write the query like this:
SELECT * FROM Survey WHERE quant='sal' AND person!='lake';
but this query filters omits the records where we don’t know who took the measurement. Once again, the reason is that when
!= comparison produces
null, so the record isn’t kept in our results. If we want to keep these records we need to add an explicit check:
SELECT * FROM Survey WHERE quant='sal' AND (person!='lake' OR person IS NULL);
We still have to decide whether this is the right thing to do or not. If we want to be absolutely sure that we aren’t including any measurements by Lake in our results, we need to exclude all the records for which we don’t know who did the work.
In contrast to arithmetic or Boolean operators, aggregation functions that combine multiple values, such as
null values. In the majority of cases, this is a desirable output: for example, unknown values are thus not affecting our data when we are averaging it. Aggregation functions will be addressed in more detail in the next section.
Sorting by Known Date
Write a query that sorts the records in
Visited by date, omitting entries for which the date is not known (i.e., is null).
NULL in a Set
What do you expect the query:
SELECT * FROM Visited WHERE dated IN ('1927-02-08', NULL);
to produce? What does it actually produce?
Pros and Cons of Sentinels
Some database designers prefer to use a sentinel value) to mark missing data rather than
null. For example, they will use the date “0000-00-00” to mark a missing date, or -1.0 to mark a missing salinity or radiation reading (since actual readings cannot be negative). What does this simplify? What burdens or risks does it introduce?